The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A fast and accurate face detector based on neural networks
Abstract
Detecting faces in images with complex backgrounds is a difficult task. Our approach, which obtains state of the art results, is based on a neural network model: the constrained generative model (CGM). Generative, since the goal of the learning process is to evaluate the probability that the model has generated the input data, and constrained since some counter-examples are used to increase the quality of the estimation performed by the model. To detect side view faces and to decrease the number of false alarms, a conditional mixture of networks is used. To decrease the computational time cost, a fast search algorithm is proposed. The level of performance reached, in terms of detection accuracy and processing time, allows us to apply this detector to a real world application: the indexing of images and videos.
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Citations
Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · cited 1×
“If all the CGM estimators are identically and independently distributed, then, the variance of the generalization error of the ensemble is divided by a factor N [ 16 ].”
BackgroundNeural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Original approach for the localisation of objects in images
Vaillant, R. (1994) · IEE Proceedings - Vision, Image, and Signal Processing · doi:10.1049/ip-vis:19941301 · Cited by 280
Cited by
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,427
Facial expression recognition – A real time approach
Geetha, A., Ramalingam, V., Palanivel, S., & Palaniappan, B. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.09.002 · Cited by 57
Metadata
- Authors
- Feraund, R., Bernier, O. J., Viallet, J.-E., & Collobert, M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2001
- Volume
- 23
- Issue
- 1
- Pages
- 42-53
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2170003605
- Cited by
- 292
- References
- 5
- FWCI
- 32.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- Feraund, R., Bernier, O. J., Viallet, J.-E., & Collobert, M. (2001). A fast and accurate face detector based on neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.899945
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